As well-known in the art, a method of classifying objects to be classified by making a database of the objects allows for accurate and fast classification but has a fatal disadvantage that an object not made for the database cannot be classified. This is a serious disadvantage for contents increasing over time.
Meanwhile, a machine learning-based classification method allows for classification of an unknown object, as well as a known object, through models generated based on machine learning, so it is commonly used for classifying and determining unspecified objects.
Determination through models based on machine learning, rather than the database scheme, is more effective in determining harmful multimedia content or a harmful section of multimedia content.
However, the machine learning-based model scheme has shortcomings in that accuracy of classification cannot be 100%. Thus, technical efforts of increasing the accuracy of determination up to nearly perfect have been actively made, but the 100% determination accuracy cannot be achieved, so the machine learning-based model scheme has a potential determination error in any event.
In determining the harmful multimedia content based on machine learning, conventional techniques mostly aim at increasing determination accuracy of a basic determination unit (minimum harmfulness determination unit) of an analysis and determination by using models generated based on machine learning in order to increase the accuracy of determination. For example, they improve the accuracy of a determination section by enhancing the characteristics to be used in the determination of a basic determination unit, or the like.
However, the conventional techniques of determining harmfulness of a basic determination unit have a problem in that it cannot decrease a local harmful content determination error to a satisfactory level.